Field of the Invention
This application relates generally to methods for image analysis of tissue sections. More specifically, this application relates to deriving a normalization value for quantification of biomarker expression in tissues evaluated with a tissue-based assay based on tissue area determined by digital image analysis of said tissues.
Description of the Related Art
Histologic evaluation of biomarker expression levels in tissue often requires normalization to a value which captures the context of the tissue. For example, the frequency of biomarker-positive cells can be determined relative to the total number of cells contained within a tissue section. Similarly, scoring paradigms such as the H-score are structured to evaluate staining intensity (i.e., a graded 0, 1, 2, 3+ scheme) with normalization to the frequency of cells expressing the biomarker at each level.
Methods to determine tissue area for manual scoring paradigms have been limited to scoring schemes which evaluate a small portion of a sample (e.g., high powered field), and are limited in their ability to exclude area which is not tissue (e.g., clear glass exposed when a tissue tears in an area) or when there is a staining or histology artifact present and the region should be excluded from analysis.
Digital image analysis tools enable processing of whole slide images of tissues in a single analysis. Current image analysis tools determine tissue area based upon assessment of individual pixels rather than assessment of tissue objects or by the local area features of multiple pixels or non-tissue object clusters of pixels.
Often, these tools can define a tissue area based on the placement of manual annotations which specify the tissue regions for analysis. While the area within the annotation is easily calculated by the tools which enable the annotation, the annotation often includes area that is not constituted of target tissue (e.g., empty space, vasculature, stroma, etc.). Therefore, there is a need to establish the effective area of tissue relevant to the analysis. Most current tools can determine tissue area by defining a color, or color intensity threshold, which distinguishes clear glass area from that occupied by the tissue section. Similar to manual scoring, these methods are typically unable to accurately distinguish between tissue objects (e.g., cells, vacuoles, airways) which may or may not contribute to overall tissue area. Furthermore, current methods do not enable definition of tissue area based on classification of tissue objects (e.g., vessels, airways, glands), classified tissue object clusters (e.g., tumor cell nests), pixel neighborhood features, or non-tissue object clusters of pixels (e.g., pixels evaluated relative to their neighborhood).
FIG. 1 provides an illustrative example of current tissue area quantification approaches whereby tissue area is determined by areas of staining above background or clear area, which is defined based on individual pixel intensity values for an IHC stain. In this example, the tissue area of interest contains tumor epithelium and TME tissue compartments where it is of interest to assess biomarker expression in each tissue compartment individually (FIG. 1A). The typical approach applied by current methodologies utilizes a threshold based on pixel intensities for the defined color to differentiate tissue area from clear glass area. FIGS. 1B and 1C illustrate the result of this image segmentation to determine tissue area whereby two different thresholds for the pixel intensities are set. This approach provides a tissue area value which does not capture tissue area in the context (e.g., tumor tissue area vs. TME tissue area) of the tissue compartments of interest for analysis.
FIG. 2 provides another example of the prior art whereby a histologic stain is retained preferentially in tumor epithelium cells. This staining can be specific or non-specific for a particular analyte. In this example, tissue area can be defined based on pixel intensities of the histologic stain above a defined threshold and in the context of the surrounding pixel neighborhood. FIG. 2A illustrates the brightfield image of the tissue section stained with two histologic stains (i.e., hematoxylin and DAB) and FIG. 2B illustrates the algorithm-based isolation of the DAB stain. The pixel intensities of the DAB stain are evaluated, and FIG. 2C illustrates a typical identification of tissue area based upon simple segmentation of individual pixel intensities. In FIG. 2C, the black outline indicating those regions with individual pixel intensities above threshold includes many gaps or holes within the tumor epithelium area, and includes regions of TME tissue area which are above threshold for individual pixels which would be considered non-target tissue. FIGS. 2D-F illustrate the incorporation of pixel neighborhood features to define tissue area. FIG. 2D illustrates the tumor tissue area (dark mark-up) without minimal accounting for pixel neighborhood, and the resulting tissue area definition by this approach is similar to that of conventional approaches. In FIGS. 2E and 2F, however, additional neighborhood features (e.g., define regions based on clusters of above threshold features, define minimum region size, define minimum number of positive pixels needed for tissue area) are evaluated which results in increasingly accurate detection and evaluation of tumor epithelium tissue area only (dark mark-up) and accounts for local variability and heterogeneity in DAB staining.